Novel adaptive stability enhancement strategy for power systems based on deep reinforcement learning

As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains

Computational Studies on the Selective Polymerization of Lactide Catalyzed by Bifunctional Yttrium NHC Catalyst

A theoretical investigation of the ring-opening polymerization (ROP) mechanism of rac-lactide (LA) with an yttrium complex featuring a N-heterocyclic carbine (NHC) tethered moiety is reported. It was found that the carbonyl of lactide is attacked by N(SiMe3)2 group rather than NHC species at the chain initiation step. The polymerization selectivity was further investigated via two consecutive insertions of lactide monomer molecules. The insertion of the second monomer in different assembly modes indicated that the steric interactions between the last enchained monomer unit and the incoming monomer together with the repulsion between the incoming monomer and the ligand framework are the primary factors determining the stereoselectivity. The interaction energy between the monomer and the metal center could also play an important role in the stereocontrol

Integrating safety culture into OSH risk mitigation via information technologies

The occupational safety and health (OSH) risk management continues to be a key topic for worldwide industries, especially in the construction industry. Most of prior technologies were used to directly but passively impact safe working engineering in a technical way. However, technologies may also indirectly but actively influence workers’ safe performance in a managerial way. This research aimed to examine this severe safety problem in the construction industry, and taking which as an example, prevention measures for OSH risk mitigation are discussed. The analyses find that large portion of the OSH fatalities and injuries are related to workers’ unsafe acts and lack of awareness on the OSH hazards. The lack of effectiveness was ascribed the OSH training content as well as the one-off nature of construction industry. An innovative approach is proposed, which integrates the safety culture into the OSH risk mitigation via the application of cutting-edge information technologies. Particularly, a virtual reality-based pilot application which links the safety culture and risk mitigation is demonstrated

A Wind Power/Photovoltaic/Hydropower/Pumped Storage Power Station System Sizing Strategy

In order to cope with the increasingly serious energy shortage, the energy system towards "zero carbon"is undoubtedly the basis for alleviating energy shortages. This study innovative proposes a two-layer planning model integrating sizing and operation optimization, with zero carbon emission and system revenue as the target, and relying on Particle Swarm Optimization (PSO) and Distributional Robust Optimization (DRO). The method to solve the problem of the sizing of power station systems under the uncertainty of scenery output, and to ensure the grid connection of renewable energy under the premise of satisfying the load demand.</p

Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS

Image Representation Method Based on Relative Layer Entropy for Insulator Recognition

Deep convolutional neural networks (DCNNs) with alternating convolutional, pooling and decimation layers are widely used in computer vision, yet current works tend to focus on deeper networks with many layers and neurons, resulting in a high computational complexity. However, the recognition task is still challenging for insufficient and uncomprehensive object appearance and training sample types such as infrared insulators. In view of this, more attention is focused on the application of a pretrained network for image feature representation, but the rules on how to select the feature representation layer are scarce. In this paper, we proposed a new concept, the layer entropy and relative layer entropy, which can be referred to as an image representation method based on relative layer entropy (IRM_RLE). It was designed to excavate the most suitable convolution layer for image recognition. First, the image was fed into an ImageNet pretrained DCNN model, and deep convolutional activations were extracted. Then, the appropriate feature layer was selected by calculating the layer entropy and relative layer entropy of each convolution layer. Finally, the number of the feature map was selected according to the importance degree and the feature maps of the convolution layer, which were vectorized and pooled by VLAD (vector of locally aggregated descriptors) coding and quantifying for final image representation. The experimental results show that the proposed approach performs competitively against previous methods across all datasets. Furthermore, for the indoor scenes and actions datasets, the proposed approach outperforms the state-of-the-art methods